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Highlights Cerium methacrylate was prepared and ingeniously used as curing agent for epoxy, surface modifier for BN, and effective corrosion inhibitor. The coating integrates high thermal conductivity (4.29 W m−1 K−1), corrosion resistance (low-frequency impedance is 5.1 × 1011 Ω cm2 at 181 day), hydrophobicity (WCA: ~ 148.35° and WSA: ~ 20°), temperature resistance and adhesion. The coating can extend service life of metal materials without sacrificing their inherent heat conduction property.

With the phasor measurement units (PMUs) being widely utilized in power systems, a large amount of data can be stored. If transient stability assessment (TSA) method based on the deep learning model is trained by this dataset, it requires high computation cost. Furthermore, the fact that unstable cases rarely occur would lead to an imbalanced dataset. Thus, power system transient stability status prediction has the bias problem caused by the imbalance of sample size and class importance. Faced with such a problem, a TSA model based on the sample selection method is proposed in this paper. Sample selection aims to optimize the training set to speed up the training process while improving the preference of the TSA model. The typical samples which can accurately express the spatial distribution of the raw dataset are selected by the proposed method. Primarily, based on the location of training samples in the feature space, the border samples are selected by trained support vector machine (SVM), and the edge samples are selected by the assistance of the approximated tangent hyperplane of a class surface. Then, the selected samples are input to stacked sparse autoencoder (SSAE) as the final classifier. Simulation results in the IEEE 39-bus system and the realistic regional power system of Eastern China show the high performance of the proposed method.

Due to their environmental protection and high power generation efficiency, the control technology of hydrogen fuel cells (HFCs) connected to the microgrid has become a research hotspot. However, when they encounter peak demand or transient events, the lack of power cannot be compensated immediately by HFCs which results in sudden changes of the voltage and frequency. The improved virtual synchronous generator (VSG) control strategy based on HFCs and supercapacitors (SCs) combined power generation system is proposed to overcome this shortcoming in this paper. The small-signal model for designing the combined system parameters is provided which are in accordance with the system loop gain, phase angle margin, and adjustment time requirements. Besides, the voltage and current double closed-loop based on sequence control is introduced in the VSG controller. The second-order generalized integrator (SOGI) is utilized to separate the positive and negative sequence components of the output voltage. At the same time, a positive and negative sequence voltage outer loop is designed to suppress the negative sequence voltage under unbalanced conditions, thereby reducing the unbalance of the output voltage. Finally, simulation results in MATLAB/Simulink environment verify that the proposed method has better dynamic characteristics and higher steady-state accuracy compared with the traditional VSG control.

BackgroundLenvatinib is a standard first-line systemic therapy in advanced hepatocellular carcinoma (aHCC) and is widely used in all lines. However, the efficacy and safety of immune checkpoint inhibitors (ICIs) plus molecular targeted agents (MTAs) after the progression of lenvatinib treatment are unclear.ObjectiveThe aim of this study was to evaluate the anticancer effects of ICI plus MTA in patients with aHCC who progressed after lenvatinib.MethodsWe retrospectively included aHCC patients treated with ICI plus MTA after the progression of lenvatinib from two medical centers. Participants who continued lenvatinib treatment were classified into the “ICI+Lenva” group, while the “ICI+Others” group included patients receiving other MTAs. The efficacy endpoints were progression-free survival (PFS), post-progression survival (PPS), overall survival (OS), and tumor response following RECIST v1.1. Safety was evaluated according to Common Terminology Criteria for Adverse Events v5.0.ResultsIn this study, 85 eligible aHCC patients were enrolled, including 58 in the ICI+Lenva group and 27 in the ICI+Others group. At a median follow-up time of 22.8 months, the median PPS and PFS were 14.0 (95% CI: 9.0-18.2) and 4.5 months (95% CI: 3.5-8.3), respectively. The objective response and disease control rates were 10.6% and 52.9%, respectively. No significant differences were observed in any of the efficacy endpoints between the two groups. Prolonged PPS was associated with Child–Pugh grade A, AFP < 400 IU/ml, and concomitant locoregional treatment. All patients experienced adverse events (AEs), but no fatal AEs were observed.ConclusionICI plus MTA in aHCC patients after the progression of lenvatinib presented high antitumor activity and safety. Patients could continue lenvatinib treatment and receive ICIs as well as locoregional treatment to achieve better OS.

Abstract Tuning the facet exposure of Cu could promote the multi-carbon (C2+) products formation in electrocatalytic CO2 reduction. Here we report the design and realization of a dynamic deposition-etch-bombardment method for Cu(100) facets control without using capping agents and polymer binders. The synthesized Cu(100)-rich films lead to a high Faradaic efficiency of 86.5% and a full-cell electricity conversion efficiency of 36.5% towards C2+ products in a flow cell. By further scaling up the electrode into a 25 cm2 membrane electrode assembly system, the overall current can ramp up to 12 A while achieving a single-pass yield of 13.2% for C2+ products. An insight into the influence of Cu facets exposure on intermediates is provided by in situ spectroscopic methods supported by theoretical calculations. The collected information will enable the precise design of CO2 reduction reactions to obtain desired products, a step towards future industrial CO2 refineries.

As a new type of Denial of Service (DoS) attacks, the Low-rate Denial of Service (LDoS) attacks make the traditional method of detecting Distributed Denial of Service Attack (DDoS) attacks useless due to the characteristics of a low average rate and concealment. With features extracted from the network traffic, a new detection approach based on multi-feature fusion is proposed to solve the problem in this paper. An attack feature set containing the Acknowledge character(ACK) sequence number, the packet size, and the queue length is used to classify normal and LDoS attack traffics. Each feature is digitalized and preprocessed to fit the input of the K-Nearest Neighbor (KNN) classifier separately, and to obtain the decision contour matrix. Then a posteriori probability in the matrix is fused, and the fusion decision index D is used as the basis of detecting the LDoS attacks. Experiments proved that the detection rate of the multi-feature fusion algorithm is higher than those of the single-based detection method and other algorithms.

In the mining process of underground metal mines, the misjudgment of rock types by on-site technicians will have a serious negative impact on the stability evaluation of rock mass and the formulation of support schemes, which will result in the loss of economic benefits and potential safety hazards of mining enterprises. In order to realize the precise and intelligent identification of rock types, the image data of peridotite, basalt, marble, gneiss, conglomerate, limestone, granite, magnetite quartzite are amplified. Under the target detection framework of Faster R-CNN deep learning, the extraction network based on simplified VGG16 is used to extract and learn features of rock images, and finally the rock type identification system is successfully trained. The experimental verification shows that the system is correct for single-type rock image recognition and the accuracy is more than 96%. In order to realize accurate and intelligent identification of the surrounding rock surface under complex lithological conditions, the multi-type rocks hybrid images are also identified. The results show that the recognition effect is great and the accuracy rate is over 80%. Therefore, this system can accurately identify rock types with similar image features, which proves that the model has strong robustness and generalization ability. It has broad application prospects in rock mass stability evaluation and rock classification in underground mining.

Denial of Service (DoS) attack is a serious threat to Software Defined Network (SDN). Although many research efforts have been devoted to identify new features for DoS attack detection, the existing approaches are not able to detect various types of DoS attacks. In SDN, DoS attacks against data plane are mainly organized in two ways: 1) DoS attack with multiple flow entries (M-DoS) to exhaust the Ternary Content-Addressable Memory (TCAM) resource of the switch. 2) DoS attack with a single well-designed entry (S-DoS) to overwhelm the target link and further impact the controller. To detect these two attacks, we propose a new approach by extracting six features of flow table, and using the back propagation (BP) neural network to construct the classifier. Test results of test-bed experiments indicate that the accurate detection probability of proposed approach is 98.9%, which can effectively distinguish M-DoS flows and S-DoS flows without being affected by Flash crowd scene.

Abstract Water‐coal interactions have gained much recent attention, although few studies focus on the strength of coal under wetting‐drying cycles. This study investigates changes in the microstructure and mechanical strength of coal that are induced by water‐coal interactions, at the macroscale and microscale. Fourteen specimens after continuous wetting and fourteen specimens after wetting‐drying cycles were tested, indicating that peak stress and elastic modulus decrease with increasing continuous wetting time and number of wetting‐drying cycles, while peak strain increases. We analyzed changes in the coal microstructure using a scanning electron microscope (SEM) and the Image Pro Plus 6.0 (IPP 6.0) software. The regression model for cyclic wetting‐drying reveals that porosity and pore circularity are the main correlation indicators associated with uniaxial compressive strength. Therefore, different numbers of wetting‐drying cycles could induce different degrees of damage to coal, which increases progressively through gradual changes to its microstructure. These findings indicate that wetting‐drying cycles have a more significant impact on the stability of coal mass than continuous wetting. Our results are of use in determining the size of coal pillars, and expanding the knowledge base related to the mechanical properties of coal mass, water bursting, water recharge channels, and the stress‐crack‐permeability evolution law in mines.

The inertia of the power system changes when the generators in the system are disturbed or tripped. However, in some systems, there is no control center or communication device falling behind. It makes real-time inertia difficult to obtain and affects the adaptive under frequency load shedding (UFLS) scheme. This paper proposes an adaptive decentralized UFLS scheme based on load information to overcome this problem. First, a calculation method is derived according to the load information after the disturbance to identify the real-time of load equivalent virtual inertia. Then, the power deficit of load is estimated and the optimal adaptive load shedding scheme is obtained. Finally, compared with conventional UFLS schemes, the proposed scheme can be performed merely by load information. Hence, the requirement of communication conditions and the dependence on the control center are reduced. The simulation is carried out on the island utilizing the IEEE39-bus system. Furthermore, the accuracy of real-time identification of load equivalent virtual inertia and the validity of the load shedding scheme are verified.

With the increasing capacity of new energy in the power system, new energy cannot provide support for the system frequency directly. This characteristic of new energy affects the frequency stability of the power system. Therefore the control strategy of a virtual synchronous generator (VSG) is proposed to improve the frequency stability of the system. An adaptive virtual inertia control strategy based on an improved bang-bang control strategy for a micro-grid is presented. On one hand, it can make full use of the variability of virtual inertia to reduce dynamic frequency deviation. On the other hand, the steady-state interval of frequency and the steady-state inertia are set to improve the system frequency stability. Then the stability analysis of the value range of the virtual inertia is performed by the small signal model of the VSG for the micro-grid. Meanwhile, the ranges of virtual inertia and steady-state inertia are determined. Finally, Matlab/Simulink is applied to accomplish simulation experiments to compare various virtual inertia control strategies. The results indicate the effectiveness of the proposed strategy.

As one of the most promising methods for H2O2 production, H2O2 electroproduction has received increasingly more attention. In this study, a spherical particle polydopamine (pDA) modified carbon felt (noted as ht-pDA/ACF) for H2O2 production was fabricated. At a constant potential of 2.0 V and pH of 1.0, the H2O2 production of the ht-pDA/ACF cathode reached 220 mg/L after 6 h of electrolyzing, compared to the 30 mg/L H2O2 production of raw carbon felt. Firstly, the spherical pDA exposes more active sites that are favorable to the 2e− ORR compared to pDA film. Secondly, the ring cleavage and re-cyclization of indole structure in the pDA during electrolyzing could form the radicals that act as the intermediate to the H2O2 formation. This research exhibits a low-cost method to modify carbon materials for effective H2O2 electroproduction. The ht-pDA/ACF cathode is promising for green H2O2 production and wastewater treatment.

A transient stability control system for the electric power system composed of a prediction method and a control method is proposed based on trajectory information. This system, which is independent of system parameters and models, can detect the transient stability of the electric power system quickly and provide the control law when the system is unstable. Firstly, system instability is detected by the characteristic concave or convex shape of the trajectory. Secondly, the control method is proposed based on the analysis of the slope of the state plane trajectory when the power system is unstable. Two control objectives are provided according to the methods of acquiring the far end point: one is the minimal cost to restore the system to a stable state; the other one is the minimal cost to limit the maximum swing angle. The simulation indicates that the mentioned transient stability control system is efficient.

Although water-injection in mature reservoirs is a promising low-cost method of enhanced oil recovery (EOR), in the process of development in the oilfield, scale has been produced in water-injection pipe columns. The ability to prevent and control the deposition of scale is critical to the efficient recovery of crude oil from hard environments, as part of the broader discipline of “flow assurance” in the petroleum industry. To this end laboratory-scale deposition tests have been useful to understand scale deposition mechanism. The process, mechanism and the main type of the scale in water-injection pipe columns of the fifth plant of the Daqing Oilfield were analyzed. The effect of temperature on the possibility of carbonate calcium formation on oil recovery was investigated experimentally. One of the scale samples was characterized by electron spectroscopy and the results of the element analysis were investigated. Moreover, the precautionary and control measures of scaling in oilfield pipe column systems are proposed.

Similar-material simulation test is an effective tool to study the practical problems in mining and civil engineering. This paper conducts an orthogonal study on low-strength similar materials comprising sand, fly ash, and plaster and analyses the sensitivity of the materials. The mechanical properties of the similar materials strongly depend on the proportioning ratio, and they can satisfy different similar-material simulation tests. The compression strength and elastic modulus of the similar material decrease as the sand-binder ratio or cement ratio increases. There are approximately linear relations between the compression strength/elastic modulus and sand-binder ratio and approximately power relations between the compression strength/elastic modulus and sand-binder ratio. Sensitivity analysis employing the range method shows that the effects of the cement ratio on the compression strength and elastic modulus are more obvious than the effects of the sand-binder ratio. Finally, one of similar materials is used in a simulation test of coal backfill mining.

Recently, there is a growing interest in the applications of wireless sensor networks (WSNs). A set of sensor nodes is deployed in order to collectively survey an area of interest and/or perform specific surveillance tasks in some of the applications, such as battlefield reconnaissance. Due to the harsh deployment environments and limited energy supply, nodes may fail, which impacts the connectivity of the whole network. Since a single node failure (cut-vertex) will destroy the connectivity and divide the network into disjoint blocks, most of the existing studies focus on the problem of single node failure. However, the failure of multiple nodes would be a disaster to the whole network and must be repaired effectively. Only few studies are proposed to handle the problem of multiple cut-vertex failures, which is a special case of multiple node failures. Therefore, this paper proposes a comprehensive solution to address the problems of node failure (single and multiple). Collaborative Single Node Failure Restoration algorithm (CSFR) is presented to solve the problem of single node failure only with cooperative communication, but CSFR-M, which is the extension of CSFR, handles the single node failure problem more effectively with node motion. Moreover, Collaborative Connectivity Restoration Algorithm (CCRA) is proposed on the basis of cooperative communication and node maneuverability to restore network connectivity after multiple nodes fail. CSFR-M and CCRA are reactive methods that initiate the connectivity restoration after detecting the node failure(s). In order to further minimize the energy dissipation, CCRA opts to simplify the recovery process by gridding. Moreover, the distance that an individual node needs to travel during recovery is reduced by choosing the nearest suitable candidates. Finally, extensive simulations validate the performance of CSFR, CSFR-M and CCRA.